Towards Optimal Guidance of Autonomous Swarm Drones in Dynamic Constrained Environments

Abstract

As autonomous drone swarms become increasingly important for complex missions, there remains a critical need for integrated approaches that can simultaneously handle task allocation and safe navigation in dynamic environments. This paper addresses the challenge of optimally allocating tasks and generating collision-free trajectories for drone swarms operating in obstacle-rich settings. Our proposed Swarm Allocation and Route Generation (SARG) framework integrates optimal task assignment with dynamically feasible trajectory planning, enabling efficient mission completion while ensuring safe navigation through complex 3D workspaces. Using quadrotors as our experimental platform, the framework incorporates both Drone-to-Obstacle and Drone-to-Drone collision avoidance algorithms, alongside a modified path planning algorithm that enhances simultaneous graph search efficiency. Our extensive experiments demonstrate that the SARG framework significantly improves performance over existing approaches. The SARG framework, while maintaining a 100% collision avoidance rate in dense environments, achieves a 21.6% reduction in the computation time of the simultaneous graph searching phase compared to conventional methods, contributing to overall system efficiency. These results establish SARG as a viable solution for real-world autonomous drone swarm applications in complex, dynamic settings. Supporting Information, including animated simulations, are available at https://youtu.be/56oabPTUz4g.